MULTIPLE REGRESSION EXAMPLE


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1 MULTIPLE REGRESSION EXAMPLE For a sample of n = 166 college students, the following variables were measured: Y = height X 1 = mother s height ( momheight ) X 2 = father s height ( dadheight ) X 3 = 1 if male, 0 if female ( male ) Our goal is to predict student s height using the mother s and father s heights, and sex, where sex is categorized using the variable male = 1 if male, 0 if female. The population model is Y i = β 0 + β 1 X i1 + β 2 X i2 + β 3 X i3 + ε i where ε i are independent, N(0, σ 2 ). NOTE: The original data has a text variable called sex with two values Male and Female. To create the indicator variable Male in Stata, the commands are:. generate male = 1. replace male = 0 if sex=="'female'" First let s look at some plots of the original data to see if there are outliers, and if the patterns look linear. See plots in extended handout on website. The plots are: G1. Separate histograms of male and female students heights. Stata: histogram height, by(sex) G2. Histogram of mothers heights. Stata: histogram momheight G3. Histogram of fathers heights. Stata: histogram dadheight G4. A scatter plot matrix (see p. 232 of text), separately for males and females. Stata: graph matrix height momheight dadheight, by(sex) or Graphics > Scatterplot matrix Examining the plots, a few possible outliers are evident: A case with momheight = inches. This is almost surely a mistake it s a female height of 6 ft, 8 inches. So it is legitimate to remove it, since we can t recover what it should be. The case is in row 129. We need to change the value to the missing value code, which is a period in Stata: replace momheight =. in 129 A case with height = 57 inches for a male. While unusual (4 ft, 9 inches) it is possible. Do not remove. A case with dadheight = 55 inches. Again this is very unusual (4 ft, 7 inches) but is possible. Do not remove. One option is to run the analysis with and without it, and see what difference it makes. (Of course you would always report that you had done that, not just chose which one you like best!) From the scatter plot matrix, we see that the relationships between the response variable height and the explanatory variables momheight and dadheight look linear, at least from what we can tell from such tiny pictures. Now let s run the regress command:. regress height momheight dadheight male We will follow that up with some plots: G5: A plot of the residuals versus fitted values: rvfplot, yline(0) G6: A normal probability plot (we need to create the variable residuals first): predict residuals, residuals qnorm residuals
2 Regress command results (remember that we now have n = 165 cases; we removed one outlier): Source SS df MS Number of obs = F( 3, 161) = Model Prob > F = Residual Rsquared = Adj Rsquared = Total Root MSE = height Coef. Std. Err. t P> t [95% Conf. Interval] momheight dadheight male _cons The regression equation (rounding coefficients to 2 decimal places) is: Predicted height = (momheight) (dadheight) (male) The coefficient for the variable male has a specific interpretation. It says that for a fixed combination of momheight and dadheight, on average males will be about 5.30 inches taller than females with that same combination of momheight and dadheight. The coefficient of about 0.30 for momheight tells us that for a given dadheight and sex, the predicted student s height increases by about 0.30 inches for every 1.0 inch increase in momheight. For example, for male students whose dads are inches tall, those whose moms are 65 inches tall are on average predicted to be about 0.30 inches taller than those whose moms are 64 inches tall. For each of the coefficients, a test for H 0 : β = 0 versus H a : β 0 has pvalue of (See the column headed P> t.) These are conditional hypotheses. They are testing whether or not each explanatory variable needs to be in the model, given that the others are already there. Therefore, in this example, the tests tell us that all 3 of the explanatory variables are useful in the model, even after the others are already in the model. In other words, even with (for example) mom s height and student s sex in the model, dad s height still adds a substantial contribution to explaining student s height. R 2 = 66.04%, which is pretty good. Later we will learn about Adjusted R 2 which can be more useful in multiple regression, especially when comparing models with different numbers of X variables. Root MSE = s = our estimate of σ = 2.32 inches, indicating that within every combination of momheight, dadheight and sex, the standard deviation of heights is about 2.32 inches. In other words, that s the estimate of the standard deviation for the population of all male students with momheight = 64 and dadheight =, for the population of all female students with those parents heights, etc, for any combination of the 3 explanatory variables. The F(3, 161) = is F* for testing the full model versus the reduced model E{Y i } = β 0. In other words, it is simultaneously testing H 0 : β 1, β 2, β 3 all = 0 versus H a : At least one is not 0. The pvalue is given as , so clearly we can reject the null hypothesis and we can conclude that at least one of the explanatory variables is useful. The residual versus fitted values and normal probability plot both show the outlier (height = 57 ) but otherwise they look like we hope they would.
3 'Female' 'Male' Density 'Height' Graphs by 'Sex' Density
4 Density 'dadheight' 'Female' 'Male' 'Height' 65 'Height' 'dadheight' 'dadheight' Graphs by 'Sex'
5 Residuals Fitted values Residuals Mom height outlier removed:
6 Residuals Fitted values The remaining outlier is a male 57 inches tall with parents heights of 61 and 66 inches (mom and dad). This could be a legitimate point so it s not okay to remove it. Normal probability plot shows the outlier too, but otherwise looks good: Residuals Inverse Normal
7
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